PerCQA: Persian Community Question Answering Dataset
This provides a new dataset for Persian language processing, enabling research in tasks like answer selection, but it is incremental as it adapts existing formats to a new language.
The authors introduced PerCQA, the first Persian dataset for Community Question Answering, containing 989 questions and 21,915 annotated answers, and built benchmarks for answer selection using pre-trained language models.
Community Question Answering (CQA) forums provide answers for many real-life questions. Thanks to the large size, these forums are very popular among machine learning researchers. Automatic answer selection, answer ranking, question retrieval, expert finding, and fact-checking are example learning tasks performed using CQA data. In this paper, we present PerCQA, the first Persian dataset for CQA. This dataset contains the questions and answers crawled from the most well-known Persian forum. After data acquisition, we provide rigorous annotation guidelines in an iterative process, and then the annotation of question-answer pairs in SemEvalCQA format. PerCQA contains 989 questions and 21,915 annotated answers. We make PerCQA publicly available to encourage more research in Persian CQA. We also build strong benchmarks for the task of answer selection in PerCQA by using mono- and multi-lingual pre-trained language models